A Preliminary Study on Constructing Decision Tree with Gene Expression Programming

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  title =        "A Preliminary Study on Constructing Decision Tree with
                 Gene Expression Programming",
  author =       "Weihong Wang and Qu Li and Shanshan Han and Hai Lin",
  booktitle =    "First International Conference on Innovative
                 Computing, Information and Control (ICICIC 2006)",
  year =         "2006",
  pages =        "222--225",
  address =      "Beijing, China",
  month =        "30 " # aug # " - 1 " # sep,
  publisher =    "IEEE Computer Society",
  bibdate =      "2007-01-05",
  bibsource =    "DBLP,
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming",
  ISBN =         "0-7695-2616-0",
  DOI =          "doi:10.1109/ICICIC.2006.22",
  abstract =     "Gene expression programming (GEP) is a kind of
                 genotype/phenotype based genetic algorithm. Its
                 successful application in classification rules mining
                 has gained wide interest in data mining and
                 evolutionary computation fields. However, current GEP
                 based classifiers represent classification rules in the
                 form of expression tree, which is less meaningful and
                 expressive than decision tree. Whats more, these
                 systems adopt one-against-all learning strategy, i.e.
                 to solve a n-class with n runs, each run solving a
                 binary classification task. In this paper, a GEP
                 decision tree(GEPDT) system is presented, the system
                 can construct a decision tree for classification
                 without priori knowledge about the distribution of
                 data, at the same time, GEPDT can solve a n-class
                 problem in a single run, preliminary results show that
                 the performance of GEP based decision tree is
                 comparable to ID3.",

Genetic Programming entries for Weihong Wang Qu Li Shanshan Han Hai Lin